ViT-PSO-SVM: Cervical Cancer Predication Based on Integrating Vision Transformer with Particle Swarm Optimization and Support Vector Machine.

Cervical cancer (CCa) is the fourth most prevalent and common cancer affecting women worldwide, with increasing incidence and mortality rates. Hence, early detection of CCa plays a crucial role in improving outcomes. Non-invasive imaging procedures with good diagnostic performance are desirable and have the potential to lessen the degree of intervention associated with the gold standard, biopsy. Recently, artificial intelligence-based diagnostic models such as Vision Transformers (ViT) have shown promising performance in image classification tasks, rivaling or surpassing traditional convolutional neural networks (CNNs). This paper studies the effect of applying a ViT to predict CCa using different image benchmark datasets. A newly developed approach (ViT-PSO-SVM) was presented for boosting the results of the ViT based on integrating the ViT with particle swarm optimization (PSO), and support vector machine (SVM). First, the proposed framework extracts features from the Vision Transformer. Then, PSO is used to reduce the complexity of extracted features and optimize feature representation. Finally, a softmax classification layer is replaced with an SVM classification model to precisely predict CCa. The models are evaluated using two benchmark cervical cell image datasets, namely SipakMed and Herlev, with different classification scenarios: two, three, and five classes. The proposed approach achieved 99.112% accuracy and 99.113% F1-score for SipakMed with two classes and achieved 97.778% accuracy and 97.805% F1-score for Herlev with two classes outperforming other Vision Transformers, CNN models, and pre-trained models. Finally, GradCAM is used as an explainable artificial intelligence (XAI) tool to visualize and understand the regions of a given image that are important for a model's prediction. The obtained experimental results demonstrate the feasibility and efficacy of the developed ViT-PSO-SVM approach and hold the promise of providing a robust, reliable, accurate, and non-invasive diagnostic tool that will lead to improved healthcare outcomes worldwide.

Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI.

Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays a crucial role in improving patient outcomes. This study introduces a non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the detection and diagnosis of prostate cancer (PCa). IVIM imaging enables the differentiation of water molecule diffusion within capillaries and outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes a two-step segmentation approach through the use of three U-Net architectures for extracting tumor-containing regions of interest (ROIs) from the segmented images. The performance of the CAD system is thoroughly evaluated, considering the optimal classifier and IVIM parameters for differentiation and comparing the diagnostic value of IVIM parameters with the commonly used apparent diffusion coefficient (ADC). The results demonstrate that the combination of central zone (CZ) and peripheral zone (PZ) features with the Random Forest Classifier (RFC) yields the best performance. The CAD system achieves an accuracy of 84.08% and a balanced accuracy of 82.60%. This combination showcases high sensitivity (93.24%) and reasonable specificity (71.96%), along with good precision (81.48%) and F1 score (86.96%). These findings highlight the effectiveness of the proposed CAD system in accurately segmenting and diagnosing PCa. This study represents a significant advancement in non-invasive methods for early detection and diagnosis of PCa, showcasing the potential of IVIM parameters in combination with machine learning techniques. This developed solution has the potential to revolutionize PCa diagnosis, leading to improved patient outcomes and reduced healthcare costs.

Carbon supported ternary layered double hydroxide nanocomposite for Fluoxetine removal and subsequent utilization of spent adsorbent as antidepressant.

Fluoxetine (FLX) is one of the most persistent pharmaceuticals found in wastewater due to increased use of antidepressant drugs in recent decades. In this study, a nanocomposite of ternary ZnCoAl layered double hydroxide supported on activated carbon (LAC) was used as an adsorbent for FLX in wastewater effluents. The nanocomposite was characterized using Fourier Transform Infrared Spectroscopy (FTIR), scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray diffraction (XRD), and surface area analysis (BET). The adsorption investigations showed that the maximum removal capacity was achieved at pH 10, with a 0.1 g/L adsorbent dose, 50 mL volume of solution, and at a temperature of 25 °C. The FLX adsorption process followed the Langmuir-Freundlich model with a maximum adsorption capacity of 450.92 mg/g at FLX concentration of 50 µg/mL. Density functional theory (DFT) computations were used to study the adsorption mechanism of FLX and its protonated species. The safety and toxicity of the nanocomposite formed from the adsorption of FLX onto LAC (FLX-LAC) was investigated in male albino rats. Acute toxicity was evaluated using probit analysis after 2, 6, and 24 h to determine LD50 and LD100 values in a rat model. The FLX-LAC (20 mg/kg) significantly increased and lengthened the sleep time of the rats, which is important, especially with commonly used antidepressants, compared to the pure standard FLX (7 mg/kg), regular thiopental sodium medicine (30 mg/kg), and LAC alone (9 mg/kg). This study demonstrated the safety and longer sleeping duration in insomniac patients after single-dose therapy with FLX-LAC. Selective serotonin reuptake inhibitors (SSRIs) like FLX were found to have decreased side effects and were considered the first-line mood disorder therapies.

Role of Serine arginine protein kinase 1 and Minichromosome maintenance protein 2 in predicting epithelial ovarian cancer response to treatment and prognosis.

BACKGROUND: Serine-Arginine (SR) proteins are a conserved family of proteins involved in RNA splicing and are reported to be over-expressed in multiple cancers. The aim of the study is evaluation of the expression of Serine arginine protein kinase 1 (SRPK1) and Minichromosome maintenance protein 2 (MCM2) in epithelial ovarian cancer (EOC) and their correlation with clinicopathological features, response to therapy, progression-free survival (PFS), and cancer-specific survival (CSS). METHODS: This study was carried out on surgical specimens of 65 patients diagnosed with EOC which were submitted to immunohistochemical staining by SRPK1 and MCM2 antibodies. RESULTS: About 89.2% of cases showed SRPK1 expression and its high expression was significantly associated with type II tumors and advanced stage. All cases showed nuclear immunoreaction for MCM2 with high expression in 49.2% of cases. There was a significant relationship between high values of SRPK1 H-score and percentage of MCM2. Postmenopause, type II pathology, advanced stage, absence of complete response to the treatment, resistance to platinum-based chemotherapy, and surgery done by a general surgeon were the factors affecting PFS. Response to treatment and platinum sensitivity were the most independent factors affecting patients' PFS. The factors associated with shorter CSS were suboptimal debulking, advanced stage, absence of complete response to the treatment, platinum resistance, and high SRPK1. High SRPK1 expression and platinum sensitivity were the independent factors affecting patients' CSS. CONCLUSIONS: SRPK1 is an unfavorable biomarker in EOC patients because of its association with aggressive histologic type, advanced International Federation of Gynecology and Obstetrics (FIGO) stage, and worse survival. SRPK1 could promote the proliferation of EOC by up-regulation of MCM2.

Comparative analysis of silver-nanoparticles and whey-encapsulated particles from olive leaf water extracts: Characteristics and biological activity.

Nanotechnology applications have been employed to improve the stability of bioactive components and drug delivery. Natural-based extracts, especially olive leaf extracts, have been associated with the green economy not only as recycled agri-waste but also in the prevention and treatment of various non-communicable diseases (NCDs). The aim of this work was to provide a comparison between the characteristics, biological activity, and gene expression of water extract of olive leaves (OLE), green synthesized OLE silver nanoparticles (OL/Ag-NPs), and OLE whey protein capsules (OL/WPNs) of the two olive varieties, Tofahy and Shemlali. The particles were characterized by dynamic light scattering, scanning electron microscope (SEM), and Fourier transform infrared. The bioactive compounds of the preparations were evaluated for their antioxidant activity and anticancer effect on HCT-116 colorectal cells as well as for their regulatory effects on cytochrome C oxidase (Cox1) and tumor necrosis factor α (TNF-α) genes. (OL/Ag-NPs) were found to be smaller than (OL/WPNs) with sizes of (37.46±1.85 and 44.86±1.62 nm) and (227.20±2.43 and 553.02±3.60 nm) for Tofahy and Shemlali, respectively. SEM showed that Shemlali (OL/Ag-NPs) had the least aggregation due to their highest Ƹ-potential (-31.76 ± 0.87 mV). The preparations were relatively nontoxic to Vero cells (IC50 = 151.94-789.25 µg/mL), while they were cytotoxic to HCT-116 colorectal cells (IC50 = 77.54-320.64 µg/mL). Shemlali and Tofahy OLE and Tofahy OL/Ag-NPs had a higher selectivity index (2.97-7.08 µg/mL) than doxorubicin (2.36 µg/mL), indicating promising anticancer activity. Moreover, Shemlali preparations regulated the expression of Cox1 (up-regulation) and TNF-α (down-regulation) on HCT-116 cells, revealing their efficiency in suppressing the expression of genes that promote cancer cell proliferation. (OL/Ag-NPs) from Tofahy and Shemlali were found to be more stable, effective, and safe than (OL/WPNs). Consequently, OL/Ag-NPs, especially Tofahy, are the best and safest nanoscale particles that can be safely used in food and pharmaceutical applications.

Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review.

Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.

Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review.

Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.

Endoscopic management of low output recurrent colonic fistula or leak after anterior resection for rectal cancer: a randomized controlled trial.

BACKGROUND: Colonic anastomotic leak and fistula following anterior resection surgery for rectal cancer are associated with high mortality rates. The incidence of occurrence varies from 2 to 25% and it is difficult to accurately calculate the incidence of fistula and leak post anterior resection, as most of them are asymptomatic. Endoscopic management of fistula and leak has become the first line of management after conservative management in many gastrointestinal surgical centers with the advantages of being less invasive, shorter length of post-operative hospital stay, effective and rapid recovery in comparison to revision surgery. Effective endoscopic management for colonic fistula or leak depends on the clinical status of the patient and fistula characters (time-to-occur and size and site of defect), and device availability. METHODS: This prospective randomized controlled clinical trial included all patients who developed the manifestations of low output recurrent colonic fistula or leak after colonic anterior resection for rectal cancer at Zagazig University Hospital between (December 2020 and August 2022). Sample size was 78 patients divided into two equal groups. Endoscopic group (EG): included 39 patients who underwent endoscopic management. Surgical group (SG): included 39 patients who underwent surgical management. RESULTS: The investigators randomized eligible 78 patients into two groups: 39 patients in SG and 39 patients in EG. The median size of the fistula or leak was nine (range: 7-14) mm in EG, versus ten (range: 7-12) mm in SG. Clipping and Endo-stitch device were used in 24 patients versus 15 patients, respectively, in EG while primary repair with ileostomy, and resection & anastomosis were used in 15 patients versus 24 patients, respectively, in SG. Recurrence, abdominal collection, and mortality were the post procedure's complications with incidence of occurrence of 10.3, 7.7 and 0%, respectively, in EG versus 20.5, 20.5 and 2.6%, respectively, in SG. Excellent, good, and poor were the parameters for quality of life with incidence of occurrence of 43.6, 54.6 and 0%, respectively, in EG versus 28.2, 33.3 and 38.5%, respectively, in SG. Median hospital stay was one (range: 1-2) day in endoscopic group, and seven (range: 6-8) days in SG. CONCLUSION: Endoscopic intervention may offer a successful modality in managing low output recurrent colonic fistula or leak after anterior resection for rectal cancer that did not respond to conservative measures in stable patients. CLINICALTRIALS: gov ID: NCT05659446.

Longitudinal diffusion and volumetric kinetics of head and neck cancer magnetic resonance on a 1.5T MR-Linear accelerator hybrid system: A prospective R-IDEAL Stage 2a imaging biomarker characterization/ pre-qualification study.

Objectives: We aim to characterize the serial quantitative apparent diffusion coefficient (ADC) changes of the target disease volume using diffusion-weighted imaging (DWI) acquired weekly during radiation therapy (RT) on a 1.5T MR-Linac and correlate these changes with tumor response and oncologic outcomes for head and neck squamous cell carcinoma (HNSCC) patients as part of a programmatic R-IDEAL biomarker characterization effort. Methods: Thirty patients with pathologically confirmed HNSCC who received curative-intent RT at the University of Texas MD Anderson Cancer Center, were included in this prospective study. Baseline and weekly Magnetic resonance imaging (MRI) (weeks 1-6) were obtained, and various ADC parameters (mean, 5 th , 10 th , 20 th , 30 th , 40 th , 50 th , 60 th , 70 th , 80 th , 90 th and 95 th percentile) were extracted from the target regions of interest (ROIs). Baseline and weekly ADC parameters were correlated with response during RT, loco-regional control, and the development of recurrence using the Mann-Whitney U test. The Wilcoxon signed-rank test was used to compare the weekly ADC versus baseline values. Weekly volumetric changes (Δvolume) for each ROI were correlated with ΔADC using Spearman's Rho test. Recursive partitioning analysis (RPA) was performed to identify the optimal ΔADC threshold associated with different oncologic outcomes. Results: There was an overall significant rise in all ADC parameters during different time points of RT compared to baseline values for both gross primary disease volume (GTV-P) and gross nodal disease volumes (GTV-N). The increased ADC values for GTV-P were statistically significant only for primary tumors achieving complete remission (CR) during RT. RPA identified GTV-P ΔADC 5 th percentile >13% at the 3 rd week of RT as the most significant parameter associated with CR for primary tumor during RT (p <0.001). Baseline ADC parameters for GTV-P and GTV-N didn't significantly correlate with response to RT or other oncologic outcomes. There was a significant decrease in residual volume of both GTV-P & GTV-N throughout the course of RT. Additionally, a significant negative correlation between mean ΔADC and Δvolume for GTV-P at the 3 rd and 4 th week of RT was detected (r = -0.39, p = 0.044 & r = -0.45, p = 0.019, respectively). Conclusion: Assessment of ADC kinetics at regular intervals throughout RT seems to be correlated with RT response. Further studies with larger cohorts and multi-institutional data are needed for validation of ΔADC as a model for prediction of response to RT.

A two-stage renal disease classification based on transfer learning with hyperparameters optimization.

Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which affect anywhere from 1 to 15% of the global population and thus; considered one of the leading causes of chronic kidney diseases (CKD). In addition to kidney stones, renal cancer is the tenth most prevalent type of cancer, accounting for 2.5% of all cancers. Artificial intelligence (AI) in medical systems can assist radiologists and other healthcare professionals in diagnosing different renal diseases (RD) with high reliability. This study proposes an AI-based transfer learning framework to detect RD at an early stage. The framework presented on CT scans and images from microscopic histopathological examinations will help automatically and accurately classify patients with RD using convolutional neural network (CNN), pre-trained models, and an optimization algorithm on images. This study used the pre-trained CNN models VGG16, VGG19, Xception, DenseNet201, MobileNet, MobileNetV2, MobileNetV3Large, and NASNetMobile. In addition, the Sparrow search algorithm (SpaSA) is used to enhance the pre-trained model's performance using the best configuration. Two datasets were used, the first dataset are four classes: cyst, normal, stone, and tumor. In case of the latter, there are five categories within the second dataset that relate to the severity of the tumor: Grade 0, Grade 1, Grade 2, Grade 3, and Grade 4. DenseNet201 and MobileNet pre-trained models are the best for the four-classes dataset compared to others. Besides, the SGD Nesterov parameters optimizer is recommended by three models, while two models only recommend AdaGrad and AdaMax. Among the pre-trained models for the five-class dataset, DenseNet201 and Xception are the best. Experimental results prove the superiority of the proposed framework over other state-of-the-art classification models. The proposed framework records an accuracy of 99.98% (four classes) and 100% (five classes).

APOBEC3A Catalytic Inactivity Mutation Induces Tertiary Structure Destabilization.

APOBEC3A (A3A)-catalyzed DNA cytosine deamination is implicated in virus and cancer mutagenesis, and A3A is a target for small molecule drug discovery. The catalytic glutamic acid (E72) is frequently mutated in biochemical studies to characterize deamination-dependent versus deamination-independent A3A functions. Additionally, catalytically active A3A is toxic in bacterial expression systems, which adversely affects yield during recombinant A3A expression. Here, we demonstrate that mutating the catalytic glutamic acid to an isosteric glutamine (E72Q) significantly decreases the thermal stability of the protein, compared to the alanine-inactivating mutation (E72A). Differential scanning fluorimetry and mass spectrometry reveal that A3A E72Q is less thermally stable than A3A E72A or wild-type A3A. Strikingly, A3A E72Q is partially denatured at 37 °C and binds single-stranded DNA with significantly poorer affinity compared to A3A E72A. This study constitutes an important cautionary note on A3A protein design and informs that A3A E72A is the preferred catalytic inactivation mutation for most applications.

Mesoporous carbon nitride supported MgO for enhanced CO2 capture.

The growing concern about the environmental consequences of anthropogenic CO2 emissions significantly stimulated the research of low-cost, efficient, and recyclable solid adsorbents for CO2 capture. In this work, a series of MgO-supported mesoporous carbon nitride adsorbents with different MgO contents (xMgO/MCN) was prepared using a facile process. The obtained materials were tested for CO2 capture from 10 vol% CO2 mixture gas with N2 using a fixed bed adsorber at atmospheric pressure. At 25 ºC, the bare MCN support and unsupported MgO samples demonstrated CO2 capture capacities of 0.99, and 0.74 mmol g-1, respectively, which were lower than those of the xMgO/MCN composites.The incorporation of MgO into the MCN improved the CO2 uptake, and the 20MgO/MCN exhibited the highest CO2 capture capacity of 1.15 mmol g-1 at 25 °C. The improved performance of the 20MgO/MCN nanohybrid can be possibly assigned to the presence of high content of highly dispersed MgO NPs along with its improved textural properties in terms of high specific surface area (215 m2g-1), large pore volume (0.22 cm3g-1), and abundant mesoporous structure. The efffects of temperature and CO2 flow rate were also investigated on the CO2 capture performance of 20MgO/MCN. Temperature was found to have a negative influence on the CO2 capture capacity of the 20MgO/MCN, which decreased from 1.15 to 0.65 mmol g-1with temperature rise from 25 C to 150º C, due to the endothermicity of the process. Similarly, the capture capacity decreased from 1.15 to 0.54 mmol g-1 with the increase of the flow rate from 50 to 200 ml minute-1 respectively. Importantly, 20MgO/MCN showed excellent reusability with consistent CO2 capture capacity over five sequential sorption-desorption cycles, suggesting its suitability for the practical capture of CO2.

Efficient CO2 methanation using nickel nanoparticles supported mesoporous carbon nitride catalysts.

The CO2 methanation technique not only gives a solution for mitigating CO2 emissions but can also be used to store and convey low-grade energy. The basic character and large surface area of mesoporous carbon nitride, (MCN), are considered promising properties for the methanation of CO2. So, a series (5-20 wt.%) of Ni-doped mesoporous carbon nitride catalysts were synthesized by using the impregnation method for CO2 methanation. the prepared catalysts were characterized by several physicochemical techniques including XRD, BET, FT-IR, Raman spectroscopy, TEM, TGA analysis, Atomic Absorption, H2-TPR, and CO2-TPD. The catalytic performance was investigated at ambient pressure and temperature range (200-500 °C) using online Gas chromatography system. The prepared catalysts showed good performance where 15%Ni/MCN exhibited the best catalytic conversion and methane yield with 100% methane selectivity at 450 °C for investigated reaction conditions.

Prediction of Wilms' Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System.

Wilms' tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of Wilms' tumor is multimodal, including surgery, chemotherapy, and occasionally, radiation therapy. Preoperative chemotherapy is used routinely in European studies and in select indications in North American trials. The objective of this study was to build a novel computer-aided prediction system for preoperative chemotherapy response in Wilms' tumors. A total of 63 patients (age range: 6 months-14 years) were included in this study, after receiving their guardians' informed consent. We incorporated contrast-enhanced computed tomography imaging to extract the texture, shape, and functionality-based features from Wilms' tumors before chemotherapy. The proposed system consists of six steps: (i) delineate the tumors' images across the three contrast phases; (ii) characterize the texture of the tumors using first- and second-order textural features; (iii) extract the shape features by applying a parametric spherical harmonics model, sphericity, and elongation; (iv) capture the intensity changes across the contrast phases to describe the tumors' functionality; (v) apply features fusion based on the extracted features; and (vi) determine the final prediction as responsive or non-responsive via a tuned support vector machine classifier. The system achieved an overall accuracy of 95.24%, with 95.65% sensitivity and 94.12% specificity. Using the support vector machine along with the integrated features led to superior results compared with other classification models. This study integrates novel imaging markers with a machine learning classification model to make early predictions about how a Wilms' tumor will respond to preoperative chemotherapy. This can lead to personalized management plans for Wilms' tumors.

Novel Bromo and methoxy substituted Schiff base complexes of Mn(II), Fe(III), and Cr(III) for anticancer, antimicrobial, docking, and ADMET studies.

In this study, four new Mn(II), Fe(III), and Cr(III) complexes with two Schiff base ligands namely, 4-bromo-2-[(E)-{[4-(2-hydroxyethyl)phenyl]imino}methyl]phenol (HL1) and 2-[(E)-{[4-(2-hydroxyethyl)phenyl]imino}methyl]-4-methoxy phenol (HL2) have been synthesized and characterized. Different analytical and spectral methods have been used to characterize the ligands and their complexes. General formulas of [M(L)Cl2(H2O)2] for FeL1, CrL1 and CrL2, and [M(L)Cl(H2O)3] for MnL2 were proposed. HOMO and LUMO energies, as well as the electrical characteristics, have been calculated using DFT/B3LYP calculations with Gaussian 09 program. The optimized lowest energy configurations of the complexes are proven. The disc diffusion technique was used to test the pharmacological activities' antibacterial efficacy against diverse bacterial and fungus species. The MTT technique was used to assess the in vitro cytotoxicity of the ligands and their metal complexes on the Hep-G2 human liver carcinoma cell line and the MCF-7 human breast cancer cell line. All compounds displayed better activity compared to the free ligands. MnL2 complex showed predominant activity when compared to the other complexes with an IC50 value of 2.6 ± 0.11 µg/ml against Hep-G2, and against MCF-7 the IC50 value was 3.0 ± 0.2 µg/ml which is less than the standard drug cisplatin (4.0 µg/ml). UV-vis electronic spectrum and gel electrophoresis techniques have been used to investigate the compounds' affinity to bind and cleavage CT-DNA. The interaction's binding constants, or Kb, have been identified, and it was discovered that the new complexes' binding affinities are in the order of FeL1 > MnL2 > CrL2 > CrL1, and the binding mechanism has been suggested. To assess the kind of binding and binding affinity of the investigated drugs with human DNA, a molecular docking study was carried out (PDB:1bna). The acquired results supported the intercalation binding mechanism proposed in the experimental part and revealed that complexes may be inserted into the DNA molecule to stop DNA replication. According to ADMET data, the synthesized compounds have a high bioavailability profile and their physicochemical and pharmacological features remained within Lipinski's RO5 predicted limitations.

Mutation-Agnostic Detection of Colorectal Cancer Using Liquid Biopsy-Based Methylation-Specific Signatures.

Detection of methylation patterns in circulating tumor DNA (ctDNA) can offer a novel approach for cancer diagnostics given the unique signature for each tumor type. We developed a next-generation sequencing (NGS)-based assay targeting 32 CpG sites to detect colorectal cancer-specific ctDNA. NGS was performed on bisulfite-converted libraries and status dichotomization was done using median methylation ratios at all targets. We included plasma samples from patients with metastatic colorectal (n = 20) and non-colorectal cancers (n = 8); and healthy volunteers (n = 4). Median methylation ratio was higher in colorectal cancer compared with non-colorectal cancers (P = .001) and normal donors (P = .005). The assay detected ctDNA in 85% of patients with colorectal cancer at a specificity of 92%. Notably, we were able to detect methylated ctDNA in 75% of patients in whom ctDNA was not detected by other methods. Detection of methylated ctDNA was associated with shorter median progression-free survival compared to non-detection (8 weeks versus 54 weeks; P = .027).

Ultrasound versus fluoroscopy-guided ureteroscopy for distal ureteric stones in adults.

Objective: To evaluate the safety and efficacy of ultrasound (US) as alternative to fluoroscopy for guidance of ureteroscopy (URS) during treatment of distal ureteric stones in adults. Materials and methods: This study enrolled 80 patients older than 18 years presented with a single distal ureteric radio-opaque stone of ≤15 mm in longest diameter. Patients were randomized and allocated into two groups: the fluoroscopy group and the ultrasound group (n = 40 patients in each group). Patients with bilateral ureteric stones, solitary kidney, ureteric congenital anomalies, history of failed ureteroscopy, history of ureteric surgery, patients with uremia and pregnant women were excluded. Patients' demographics, stone characteristics, operative data, stone-free status, hospital stay and complications were evaluated in both groups. Results: No statistically significant difference between both groups was found regarding patients' demographics and stone characteristics. Also there was no statistically significant difference in comparing fluoroscopy group versus ultrasound group regarding operative time (29.48 ± 15.3 versus 31.28 ± 18.24 min; P = 0.83), stone-free rate (97.5% versus 95%; P = 1.0), overall complications (15% versus 12.5%; P = 0.75), or hospital stay (1.17 ± 0.6 versus 1.02 ± 0.16 days; P = 0.12). Four patients (10%) in the ultrasound group required the addition of fluoroscopy beside ultrasound. Conclusion: Ultrasound is effective in guidance during ureteroscopy for distal ureteric stones. It was comparable to fluoroscopy in terms of stone free rate, operative time, overall complications, and hospital stay. However, fluoroscopy must be available to be used when needed.

A Novel System for Precise Grading of Glioma.

Gliomas are the most common type of primary brain tumors and one of the highest causes of mortality worldwide. Accurate grading of gliomas is of immense importance to administer proper treatment plans. In this paper, we develop a comprehensive non-invasive multimodal magnetic resonance (MR)-based computer-aided diagnostic (CAD) system to precisely differentiate between different grades of gliomas (Grades: I, II, III, and IV). A total of 99 patients with gliomas (M = 49, F = 50, age range = 1-79 years) were included after providing their informed consent to participate in this study. The proposed imaging-based glioma grading (GG-CAD) system utilizes three different MR imaging modalities, namely; contrast-enhanced T1-MR, T2-MR known as fluid-attenuated inversion-recovery (FLAIR), and diffusion-weighted (DW-MR) to extract the following imaging features: (i) morphological features based on constructing the histogram of oriented gradients (HOG) and estimating the glioma volume, (ii) first and second orders textural features by constructing histogram, gray-level run length matrix (GLRLM), and gray-level co-occurrence matrix (GLCM), (iii) functional features by estimating voxel-wise apparent diffusion coefficients (ADC) and contrast-enhancement slope. These features are then integrated together and processed using a Gini impurity-based selection approach to find the optimal set of significant features. The reduced significant features are then fed to a multi-layer perceptron artificial neural networks (MLP-ANN) classification model to obtain the final diagnosis of a glioma tumor as Grade I, II, III, or IV. The GG-CAD system was evaluated on the enrolled 99 gliomas (Grade I = 13, Grade II = 22, Grade III = 22, and Grade IV = 42) using a leave-one-subject-out (LOSO) and k-fold stratified (with k = 5 and 10) cross-validation approach. The GG-CAD achieved 0.96 ± 0.02 quadratic-weighted Cohen's kappa and 95.8% ± 1.9% overall diagnostic accuracy at LOSO and an outstanding diagnostic performance at k = 10 and 5. Alternative classifiers, including RFs and SVMlin produced inferior results compared to the proposed MLP-ANN GG-CAD system. These findings demonstrate the feasibility of the proposed CAD system as a novel tool to objectively characterize gliomas using the comprehensive extracted and selected imaging features. The developed GG-CAD system holds promise to be used as a non-invasive diagnostic tool for Precise Grading of Glioma.

Predicting Recurrence of Non-Muscle-Invasive Bladder Cancer: Current Techniques and Future Trends.

Bladder cancer (BC) is the 10th most common cancer globally and has a high mortality rate if not detected early and treated promptly. Non-muscle-invasive BC (NMIBC) is a subclassification of BC associated with high rates of recurrence and progression. Current tools for predicting recurrence and progression on NMIBC use scoring systems based on clinical and histopathological markers. These exclude other potentially useful biomarkers which could provide a more accurate personalized risk assessment. Future trends are likely to use artificial intelligence (AI) to enhance the prediction of recurrence in patients with NMIBC and decrease the use of standard clinical protocols such as cystoscopy and cytology. Here, we provide a comprehensive survey of the most recent studies from the last decade (N = 70 studies), focused on the prediction of patient outcomes in NMIBC, particularly recurrence, using biomarkers such as radiomics, histopathology, clinical, and genomics. The value of individual and combined biomarkers is discussed in detail with the goal of identifying future trends that will lead to the personalized management of NMIBC.